"""simple docstring"""



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__lowercase		:		int								=				logging.get_logger(__name__)

__lowercase		:		Optional[int]								=				{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
__lowercase		:		Union[str, Any]								=				[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]





def  SCREAMING_SNAKE_CASE							(  snake_case,   snake_case,   snake_case,   snake_case,   snake_case,   snake_case):

     for attribute in key.split('''.'''):
          if is_finetuned:
               if attribute in ["quantizer", "project_q", "project_hid"]:
                    # those layers are only relevant for pretraining and should be dropped
                    return

               if attribute == "ctc_proj":
                    # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
                    __snake_case				=				'''lm_head'''

          __snake_case				=				getattr(_A,   _A)

     if weight_type is not None:
          __snake_case				=				getattr(_A,   _A).shape
     else:
          __snake_case				=				hf_pointer.shape

     assert hf_shape == value.shape, (
         f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
         f" {value.shape} for {full_name}"
     )

     if weight_type == "weight":
          __snake_case				=				value
     elif weight_type == "weight_g":
          __snake_case				=				value
     elif weight_type == "weight_v":
          __snake_case				=				value
     elif weight_type == "bias":
          __snake_case				=				value
     else:
          __snake_case				=				value

     logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.")





def  SCREAMING_SNAKE_CASE							(  snake_case,   snake_case,   snake_case):
     __snake_case				=				[]
     __snake_case				=				fairseq_model.state_dict()

     __snake_case				=				hf_model.unispeech.feature_extractor

     for name, value in fairseq_dict.items():
          __snake_case				=				False
          if "conv_layers" in name:
               load_conv_layer(
                   _A,   _A,   _A,   _A,   hf_model.config.feat_extract_norm == '''group''',   )
               __snake_case				=				True
          else:
               for key, mapped_key in MAPPING.items():
                    __snake_case				=				'''unispeech.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                    if key in name or key.split('''w2v_model.''')[-1] == name.split('''.''')[0]:
                         __snake_case				=				True
                         if "*" in mapped_key:
                              __snake_case				=				name.split(_A)[0].split('''.''')[-2]
                              __snake_case				=				mapped_key.replace('''*''',   _A)
                         if "weight_g" in name:
                              __snake_case				=				'''weight_g'''
                         elif "weight_v" in name:
                              __snake_case				=				'''weight_v'''
                         elif "bias" in name:
                              __snake_case				=				'''bias'''
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              __snake_case				=				'''weight'''
                         else:
                              __snake_case				=				None
                         set_recursively(_A,   _A,   _A,   _A,   _A,   _A)
                    continue
          if not is_used:
               unused_weights.append(_A)

     logger.warning(f"Unused weights: {unused_weights}")





def  SCREAMING_SNAKE_CASE							(  snake_case,   snake_case,   snake_case,   snake_case,   snake_case):
     __snake_case				=				full_name.split('''conv_layers.''')[-1]
     __snake_case				=				name.split('''.''')
     __snake_case				=				int(items[0])
     __snake_case				=				int(items[1])

     if type_id == 0:
          if "bias" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                   f"{full_name} has size {value.shape}, but"
                   f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
               )
               __snake_case				=				value
               logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.")
          elif "weight" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                   f"{full_name} has size {value.shape}, but"
                   f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
               )
               __snake_case				=				value
               logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.")
     elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
          if "bias" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                   f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
                   " found."
               )
               __snake_case				=				value
               logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
          elif "weight" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                   f"{full_name} has size {value.shape}, but"
                   f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
               )
               __snake_case				=				value
               logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
     else:
          unused_weights.append(_A)





@torch.no_grad()
def  SCREAMING_SNAKE_CASE							(  snake_case,   snake_case,   snake_case=None,   snake_case=None,   snake_case=True):

     if config_path is not None:
          __snake_case				=				UniSpeechConfig.from_pretrained(_A)
     else:
          __snake_case				=				UniSpeechConfig()

     if is_finetuned:
          if dict_path:
               __snake_case				=				Dictionary.load_from_json(_A)

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               __snake_case				=				target_dict.pad_index
               __snake_case				=				target_dict.bos_index
               __snake_case				=				target_dict.eos_index
               __snake_case				=				len(target_dict.symbols)
               __snake_case				=				os.path.join(_A,   '''vocab.json''')
               if not os.path.isdir(_A):
                    logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_A))
                    return
               os.makedirs(_A,   exist_ok=_A)
               __snake_case				=				target_dict.indices

               # fairseq has the <pad> and <s> switched
               __snake_case				=				42
               __snake_case				=				43
               with open(_A,   '''w''',   encoding='''utf-8''') as vocab_handle:
                    json.dump(_A,   _A)
               __snake_case				=				WavaVecaPhonemeCTCTokenizer(
                   _A,   unk_token=target_dict.unk_word,   pad_token=target_dict.pad_word,   bos_token=target_dict.bos_word,   eos_token=target_dict.eos_word,   word_delimiter_token='''|''',   do_lower_case=_A,   )
               __snake_case				=				True if config.feat_extract_norm == '''layer''' else False
               __snake_case				=				WavaVecaFeatureExtractor(
                   feature_size=1,   sampling_rate=1_60_00,   padding_value=0,   do_normalize=_A,   return_attention_mask=_A,   )
               __snake_case				=				WavaVecaProcessor(feature_extractor=_A,   tokenizer=_A)
               processor.save_pretrained(_A)

          __snake_case				=				UniSpeechForCTC(_A)
     else:
          __snake_case				=				UniSpeechForPreTraining(_A)

     if is_finetuned:
          __snake_case		,						__snake_case		,						__snake_case				=				fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path],   arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''')[:-1]), '''w2v_path''': checkpoint_path})
     else:
          __snake_case		,						__snake_case		,						__snake_case				=				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path])

     __snake_case				=				model[0].eval()

     recursively_load_weights(_A,   _A,   _A)

     hf_unispeech.save_pretrained(_A)


if __name__ == "__main__":
       __lowercase		:		int								=				argparse.ArgumentParser()
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
       parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
       parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
       parser.add_argument(
           "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
       )
       __lowercase		:		Dict								=				parser.parse_args()
       convert_unispeech_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )

"""simple docstring"""



import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


a       :						int       		=		5_0_0_0_0_0

a		,			a       :						Union[str, Any]       		=		os.path.split(__file__)
a       :						Dict       		=		os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))


@get_duration
def 			_UpperCamelCase						(    _A       ,   **_A   )     ->	Any:





    """simple docstring"""

    _UpperCAmelCase        = dataset.map(**_A   )


@get_duration
def 			_UpperCamelCase						(    _A       ,   **_A   )     ->	Dict:





    """simple docstring"""

    _UpperCAmelCase        = dataset.filter(**_A   )


def 			_UpperCamelCase						(    )     ->	Tuple:





    """simple docstring"""

    _UpperCAmelCase        = {"""num examples""": SPEED_TEST_N_EXAMPLES}
    with tempfile.TemporaryDirectory() as tmp_dir:
        _UpperCAmelCase        = datasets.Features({"""text""": datasets.Value("""string"""   ), """numbers""": datasets.Value("""float32"""   )}   )
        _UpperCAmelCase        = generate_example_dataset(
            os.path.join(_A       ,   """dataset.arrow"""   )       ,   _A       ,   num_examples=_A   )

        _UpperCAmelCase        = transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""       ,   use_fast=_A   )

        def tokenize(_A   ):
            return tokenizer(examples["""text"""]   )

        _UpperCAmelCase        = map(_A   )

        _UpperCAmelCase        = map(_A       ,   batched=_A   )

        _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""numpy"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""pandas"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""torch"""       ,   columns="""numbers"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""tensorflow"""       ,   columns="""numbers"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        _UpperCAmelCase        = map(_A       ,   function=_A       ,   batched=_A   )

        _UpperCAmelCase        = filter(_A   )

        # Activate later when tokenizer support batched inputs
        # with dataset.formatted_as(type='numpy'):
        #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

    with open(_A       ,   """wb"""   ) as f:
        f.write(json.dumps(_A   ).encode("""utf-8"""   )   )


if __name__ == "__main__":  # useful to run the profiler
 benchmark_map_filter()